Altering cough reflex sensitivity with aerosolized capsaicin paired with behavioral cough suppression: a proof-of-concept study

Slovarp, Laurie; Bozarth, Emma

doi:10.21037/atm.2018.12.26

Cited by 14 publications

(15 citation statements)

References 51 publications

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“…ANNs have shown highly promising results in several areas of healthcare such as medical imaging, information extraction from medical texts and electronic health records, and combining several types of input into one predictive model [5]. Also tree boosting has shown high performance across several medical domains [41]. Tree boosting algorithms are also much easier to train than artificial neural nets and their performance is quite immune to feature scaling and collinearity issues.…”

Section: Plos Onementioning

confidence: 99%

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

et al. 2020

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State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods.

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Section: Plos Onementioning

confidence: 99%

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

et al. 2020

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“…The researchers thought that may related to the desensitization of TRPV1. Moreover, Slovarp and Bozarth 55 paired CS strategies with inhalation of aerosolized capsaicin given in gradually increasing doses in 5 healthy people and found that C5 was significantly increased at both 1 and 3 weeks post-treatment. However, the sample size of these three studies were small, and further research is needed to investigate the relationship between capsaicin exposure and decreased cough sensitivity.…”

Section: Evaluation Methods For Cough Sensitivitymentioning

confidence: 99%

Evaluation methods and influencing factors of cough sensitivity

Mei

Ran

et al. 2022

Ther Adv Respir Dis

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Increased cough sensitivity is an important mechanism of chronic cough, and the evaluation of cough sensitivity helps understand the mechanism of cough and explore better methods to reduce cough. Evaluation methods may be direct or indirect. Direct methods include mechanical stimulation and chemical stimulation, and indirect methods include laryngeal reflex test, questionnaires, and brain functional magnetic resonance imaging (fMRI). Chemical stimulation is the most common method, while the capsaicin cough challenge test is proven and widely used. In this article, we will compare evaluation methods and explore influencing factors of cough sensitivity.

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“… Random forest 17 , 18 with the Gini impurity index G 19 , 20 as the node splitting metrics. Gradient boosting 21 , 22 using Gini impurity index for updating the decision tree. Logistic regression 23 – 25 with respect to the target class, applied to the results of forward stepwise regression 26 of a gradually reduced set of pairwise (R-squared) correlations 27 .…”

Section: Methodsmentioning

confidence: 99%

Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

Profant

Bureš

Balogová

et al. 2021

Sci Rep

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Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient’s preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the need of active treatment?, and which attributes of VS development are important in decision making on active treatment? Using a machine-learning analysis of medical records of 93 patients, the objectives were addressed using two classification tasks: a time-independent case-based reasoning (CBR), where each medical record was treated as independent, and a personalized dynamic analysis (PDA), during which we analyzed the individual development of each patient’s state in time. Using the CBR method we found that Koos classification of tumor size, speech reception threshold, and pure tone audiometry, collectively predict the need for active treatment with approximately 90% accuracy; in the PDA task, only the increase of Koos classification and VS size were sufficient. Our results indicate that VS treatment may be reliably predicted using only a small set of basic parameters, even without the knowledge of individual development, which may help to simplify VS treatment strategies, reduce the number of examinations, and increase cause effectiveness.

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Altering cough reflex sensitivity with aerosolized capsaicin paired with behavioral cough suppression: a proof-of-concept study

Cited by 14 publications

References 51 publications

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

Evaluation methods and influencing factors of cough sensitivity

Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

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